Abstract
Objective
To evaluate the relation between corneal optical quality and ocular surface manifestations in Chinese female patients with Sjogren's syndrome dry eye (SSDE).
Methods
Cross-sectional study of female SSDE patients. Demographic information and ophthalmological and rheumatological indicators were collected. Ocular Surface Disease Index (OSDI) and Chinese Dry Eye Questionnaire (CDEQ), best-corrected visual acuity (BCVA), first and mean noninvasive tear break-up time (FNIBUT and MNIBUT), Schirmer I testing, Oxford Staining Score (OSS), meibomian gland loss (MGL), and optical quality were evaluated using generalized estimating equation (GEE) models. Multinomial logistics regression models and multiple linear regression models were employed to assess the correlations between dry eye indicators and corneal optical quality.
Results
27 SSDE patients (47 eyes), 9 Non-Sjögren’s Syndrome dry eye patients (NSSDE, 14 eyes), and 23 normal controls (NC, 44 eyes) were included. More severe dry eye signs and poorer results of corneal optical qualities were found in Chinese female SSDE patients (all p < 0.05). More severe dry eye signs (CDEQ score, FNIBUT, MNIBUT, OSS, Schirmer I test, lipid layer distribution, and MGL) and poorer results of corneal optical qualities (angle α) were found in SSDE patients (all p < 0.05). In addition, there was a significant difference in astigmatism (posterior corneal surface astigmatism, and the types of astigmatism on the anterior and posterior surface of the cornea) between the groups (all p < 0.05).
Conclusions
Chinese females with DE, and particularly those with concurrent SS, demonstrated poorer ocular surface and corneal optical quality measures than those without DE.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12886-025-04225-2.
Keywords: Sjogren's syndrome, Dry eye, Ocular surface, Optical quality, Chinese female
Introduction
Dry eye (DE) disease is a common, chronic, inflammatory condition. According to previous studies, women are more susceptible to DE compared to men, and the prevalence increases with age [1]. Dry eye (DE) is a common, chronic, inflammatory condition that affects the ocular surface and tear film, leading to discomfort, visual impairment, and reduced quality of life [2]. This disease can trigger changes in the ocular surface and tear film [3, 4], which in turn can increase ocular discomfort, fatigue, and visual impairment that can interfere with a patient's daily activities, such as reading, driving, and electronics use, to varying degrees [1]. DE disease can cause corneal damage, such as by inducing inflammation, protein and lipid deposition, corneal edema, and corneal neovascularization. This adversely affects corneal refraction and the optical pathway to the retina, and in severe cases, it can even lead to blindness [5]. Currently, abnormal visual function is included in the definition and diagnostic criteria of DE disease by Asia Dry Eye Society [6].
Certain systemic diseases, such as Sjogren's syndrome (SS), are considered to be a risk factor for DE disease [7]. This systemic autoimmune disease involves the exocrine glands and is primarily associated with immune cell dysregulation [8, 9]. The disease has an insidious onset and diverse clinical manifestations, with up to 98% of patients presenting with dryness of various organ systems [10]. Many patients present with dry mouth and eyes due to decreased function of the salivary and lacrimal glands [11]. SS, an autoimmune disease, is a significant risk factor for DE, often presenting with severe ocular surface manifestations due to decreased tear production and increased tear film instability [11]. Tear film instability and ocular surface inflammation can lead to a vicious cycle that complicates the treatment of DE disease [12, 13]. If this disease is not treated in a timely manner, the patient's symptoms may worsen, become more difficult to treat, and may lead to permanent ocular damage.
Previous studies have explored the impact of DE on corneal optical quality [14], but the relationship remains incompletely understood, particularly in Chinese female patients with SSDE. This study aims to fill this gap by evaluating the correlation between corneal optical quality and clinical ocular surface manifestations in Chinese female patients with SSDE. This research aims to elucidate the relationship between corneal optical quality and clinical ocular surface manifestations in Chinese females with Sjogren's syndrome dry eye (SSDE), and to identify potential indicators for early intervention to preserve visual quality.
Methods
Study design and participants
This cross-sectional case–control study was performed according to the principles of the Declaration of Helsinki and was approved by the Ethics Committee of the Second Hospital of Guangzhou Medical University (2019-hs-12).
We recruited 27 SSDE patients (47 eyes), 9 Non-Sjögren’s Syndrome dry eye patients (NSSDE, 14 eyes), and 23 normal controls (NC, 44 eyes) from July 2021 to December 2023. Flowchart of participant screening was shown in Fig. 1. The diagnosis and classification of SS patients were identified by a rheumatologist (YSL) in accordance with the 2016 American College of Rheumatology/European League Against Rheumatism classification criteria for primary Sjogren's syndrome [15]. The inclusion criteria included the following: (1) aged 20–60 years; (2) intraocular pressure (IOP) < 21 mmHg; (3) no other systemic or ocular diseases. Exclusion criteria included the following: ocular anatomical abnormalities (eyelid entropion, eyelid scarring, etc.); history of glaucoma; contact lenses wearers; intraocular surgery within the last 12 months; recent eye infection; and pregnant or lactating women. The sample size for the Non-Sjögren’s Syndrome dry eye (NSSDE) group was limited due to the specific inclusion criteria and the relative rarity of this subgroup among the overall dry eye population. While NSSDE patients are encountered in clinical practice, recruiting a larger sample size within the study timeframe was challenging. Future studies may focus on expanding this cohort to provide more robust data.
Fig. 1.
Flowchart of study participants. SSDE, Sjogren's syndrome dry eye. NSS, Non-Sjögren’s Syndrome dry eye. Some patients who were missing data on several essential indicators (e.g., best-corrected visual acuity, ocular surface staining, etc.) were excluded
Patients were included based on the presence of clinically significant dry eye symptoms and signs in at least one eye. While dry eye disease (DED) often affects both eyes, particularly in patients with Sjogren's syndrome, the severity and manifestations can vary between eyes. Therefore, both eyes were included if they met the inclusion criteria, while only one eye was included if the other did not meet the criteria or if the patient had unilateral disease.
All the participants received a standardized ophthalmological examination consisting of best corrected visual acuity (BCVA), IOP, and slit-lamp examination at the time of enrollment.
Dry eye questionnaire collection
Participants were instructed to fill out the Ocular Surface Disease Index (OSDI) and Chinese Dry Eye Questionnaire, which required them to describe the impact of their current symptoms on their daily lives. The OSDI ranged from 0–100, while the Chinese Dry Eye Questionnaire ranged from 0–48. An OSDI score of less than 20 is considered normal or mild dry eye; 20–45 is considered moderate; and more than 45 is considered severe dry eye [16]. An CDEQ score of more than 7 is considered as symptomatic DE [17].
Ocular surface measures
We examined tear meniscus height (TMH), staining of the ocular surface, first and mean noninvasive tear break-up time (FNIBUT and MNIBUT), lipid layer condition, bulbar conjunctival hyperemia (BCH), and the condition of meibomian gland loss (MGL). The above subjects were measured and evaluated using a Keratograph 5 M (Oculus, Wetzlar, Germany), which has a high level of discrimination and diagnostic accuracy [18]. The staining map obtained by Oculus was scored in conjunction according to the Oxford Staining Score(OSS) to assess the damage to the ocular surface [19]. In addition, the best corrected visual acuity (BCVA) and intraocular pressure (IOP) of all patients were tested before the ocular surface examination. Best-corrected visual acuity (BCVA) was measured and converted to LogMAR for analysis. Original measurements were taken using a standard ETDR chart. The conversion formula is:logMAR = log10 (1/visual acuity value)".
Examinations were conducted in a controlled environment with a temperature of 22-25 ℃ and humidity of 40–60%. Participants were acclimated to the room for at least 15 min before testing. Ocular surface tests were performed in the following order: tear meniscus height (TMH), first and mean noninvasive tear break-up time (FNIBUT and MNIBUT), meibomian gland loss (MGL), ocular surface staining, and Schirmer I test. This sequence minimizes the impact of eyelid manipulation and fluorescein instillation on subsequent measurements. Lipid layer distribution was assessed using the Keratograph 5 M (Oculus, Wetzlar, Germany) and graded based on the presence and uniformity of the lipid layer. Meibomian gland loss was evaluated using the meiboscore system validated by Reiko Arita [20].
Based on previous study, fluorescein staining with the yellow filter has the advantage of simultaneous observation of both corneal and conjunctival damage in patients with dry eye without the need for additional vital staining [21]. Corneal staining was assessed 2–3 min after fluorescein instillation to ensure stable conditions. The Schirmer I test was performed with the eyes closed to standardize tear production measurement. Fluorescein strips (Oculus, Wetzlar, Germany) and Schirmer test strips (Tianjin Jingming New Technology Development Co., Ltd., Tianjin, China) were used.
The Schirmer I test without anesthesia was used to measure tear production. A dry Schirmer test strip was inserted over the outer one-third of the lower eyelid margin and the distance that the tears traveled along the test strip at 5 min was recorded as the Schirmer I score.
Corneal optical quality measures
The Pentacam (Oculus, Wetzlar, Germany) was used to assess corneal morphology, including astigmatism on the anterior and posterior surfaces. The iTrace wavefront aberrometer (Tracey Technologies, Houston, TX, USA) was used to directly measure corneal optical quality, including total corneal high-order aberrations (tHOAs), spherical aberrations (SAs), comas, modulation transfer function (MTF), and angles α and κ. These measurements provide a comprehensive evaluation of corneal optical quality.
Statistical analysis
The statistical power was calculated using PASS 15.0 software. Stata statistical software (V.17.0, Stata Corp, College Station, TX) was used for statistical analyses in this study, and p < 0.05 was considered a statistically significant difference. Means, standard errors, and weighted percentages were used for continuous variables, and frequency and weighted percentages were used for categorical variables. Generalized estimating equation (GEE) models were used to count for inter-correlation of eyes within study subjects. Eyes (left or right) were set as within-subject variables in the GEE models. The measurements were dependent variables, while age, and eye were set as covariates. Normality of distribution was verified using the Kolmogorov–Smirnov test. Given the small sample size of the NSSDE group, normality tests were performed on each dataset. For variables that did not follow a normal distribution, data were presented as median (interquartile range, Q1-Q3) to accurately reflect the central tendency and dispersion (Supplementary Table 1).
Group comparisons for normally distributed continuous variables were performed using GEE. Kruskal–Wallis tests were utilized to analyze non-normally distributed data. Categorical data were compared using χ2 tests. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to assess the predictive ability of statistically significant ocular surface indicators for DE disease. To explore the relationship between ocular surface indicators and corneal optical quality indicators and between-group differences, prior to conducting regression analysis, associations between dependent and independent variables were assessed using Kendall's tau-b correlation coefficient. For multiple linear regression analysis, the dependent variable was continuous and normally distributed (e.g., corneal optical quality indicators). For multinomial logistic regression analysis, the dependent variable was categorical, following a binomial distribution (e.g., presence or absence of specific ocular surface manifestations).
Results
Clinical ocular surface manifestations and optical quality performance
Sample size calculation was performed based on the expected effect size. In our current study, a sample size of 30 eyes in the SSDE group, 10 eyes in the NSSDE group, and 30 eyes in the NC group achieved a statistical power of 93% in differentiating the Schirmer I test, OSS, FNIBUT, and MNIBUT measurements.
In this study, participants were recruited between July 2021 and December 2023 at the Second Affiliated Hospital of Guangzhou Medical University. 27 SSDE patients (47 eyes), 9 NSSDE patients (14 eyes), and 23 NC participants (44 eyes) met the inclusion and exclusion criteria. We performed further SS disease diagnosis and corneal optical quality assessment on these participants. After diagnosis by the same rheumatologist according to the 2016 American College of Rheumatology/European League Against Rheumatism classification criteria for primary Sjogren's syndrome, the above participants were enrolled in this study. Rheumatologic indicators of SSDE patients are shown in Table 1.
Table 1.
Demographics, ocular characteristics, and rheumatologic indicators of included participants
| Characteristics | SSDE(n = 27) | NSSDE(n = 9) | NC(n = 23) | P |
|---|---|---|---|---|
| Age, mean ± SE(years) | 48.26 ± 1.88 | 39.78 ± 4.42 | 27.74 ± 1.55 | 0.001* |
| OSDI, mean ± SE | 17.023 ± 4.449 | 3.085 ± 1.085 | 5.173 ± 1.849 | 0.057 |
| CDEQ, mean ± SE | 11.143 ± 1.368 | - | 4.250 ± 1.016 | 0.001 |
| Duration, mean ± SE(years) | 2.616 ± 0.775 | 2.600 ± 1.076 | - | 0.992 |
| ESSDAI, mean ± SE | 7.680 ± 1.532 | 8.143 ± 3.262 | - | 0.891 |
|
IgM, mean ± SE (Reference Range: 0.4–2.3 g/L) |
1.136 ± 0.108 | 1.107 ± 0.330 | - | 0.913 |
|
IgA, mean ± SE (Reference Range: 0.7-4 g/L) |
3.390 ± 0.363 | 2.723 ± 0.549 | - | 0.379 |
|
IgG, mean ± SE (Reference Range: 7-16 g/L) |
16.304 ± 1.502 | 17.226 ± 3.351 | - | 0.784 |
|
GLB, mean ± SE (Reference Range: 20-40 g/L) |
33.296 ± 1.824 | 33.314 ± 3.887 | - | 0.996 |
|
ESR, mean ± SE (Reference Range: < 20 mm/h) |
35.870 ± 7.686 | 38.429 ± 13.652 | - | 0.873 |
|
CRP, mean ± SE (Reference Range: < 8 mg/L) |
8.514 ± 5.001 | 7.591 ± 6.204 | - | 0.926 |
|
RF, mean ± SE (Reference Range: < 20U/L) |
17.217 ± 3.611 | 35.242 ± 21.592 | - | 0.178 |
Abbreviations: SSDE Sjögren’s Syndrome dry eye, NSS Non-Sjögren’s Syndrome dry eye, NC normal controls, n number of participants, OSDI ocular surface disease index, CDEQ Chinese dry eye questionnaire, ESSDAI EULAR Sjögren's syndrome disease activity index. IgM Immunoglobulin M, IgA Immunoglobulin A, IgG Immunoglobulin G, GLB Globulin, ESR electron spin resonance, CRP C-reactive protein, RF Rheumatoid factor
Bold p-value represents < 0.05.* indicates that the data is the result of a Kruskal–Wallis tests
As shown in Table 2, compared to the NSSDE group and NC group, those with SSDE had decreased tear film breakup times (5.580 ± 0.529 s, p < 0.001; 9.186 ± 0.844 s, p = 0.001), and lower tear secretion (4.617 ± 0.579 mm/5 min, p < 0.001) over the same period of time. Uneven distribution of the lipid layer (p = 0.003) was observed in SSDE patients. Additionally, the upper MGL (p < 0.001) and lower MGL (p = 0.001) were worse in SSDE patients. The results of the participant questionnaire showed that the CDEQ scores of SSDE patients were higher than those of the NC group (11.143 ± 1.368, p = 0.001). BCVA was converted to logarithmic minimum angle of resolution(logMAR). There was a statistically significant difference in logMAR between the three groups of participants (p = 0.001), but this was perhaps due to the greater mean age of the SS group (p < 0.001). The ability of statistically different ocular surface indicators to discriminate SS can be reflected in Fig. 2. OSS, Schirmer I test, CDEQ, FNIBUT and MNIBUT all exhibit robust discriminatory capacity with AUCs of 0.962, 0.953, 0.861, 0,799 and 0.769, respectively.
Table 2.
Ocular surface manifestations among three groups
| Characteristics | SS | NSS | NC | P |
|---|---|---|---|---|
| (n = 47 Eyes, 44.76%) | (n = 14 Eyes, 13.33%) | (n = 44 Eyes,41.90%) | ||
| BCVA(LogMAR), mean ± SE | 0.022 ± 0.022 | −0.037 ± 0.012 | −0.082 ± 0.011 | 0.001 |
| IOP, mean ± SE(mmHg) | 14.489 ± 0.536 | 14.241 ± 0.905 | 14.687 ± 0.397 | 0.907* |
| TMH, mean ± SE(mm) | 0.187 ± 0.010 | 0.205 ± 0.022 | 0.255 ± 0.026 | 0.078 |
|
Schirmer I test, mean ± SE(mm/5 min) |
4.617 ± 0.579 | 5.357 ± 1.117 | 21.455 ± 1.224 | < 0.001 |
| OSS, mean ± SE | 7.833 ± 0.729 | 0.250 ± 0.163 | 1.265 ± 0.261 | 0.001* |
| FNIBUT, mean ± SE(s) | 5.580 ± 0.529 | 10.455 ± 2.234 | 12.620 ± 1.028 | < 0.001 |
| MNIBUT, mean ± SE(s) | 9.186 ± 0.844 | 12.457 ± 2.089 | 16.036 ± 0.890 | 0.001* |
| BCH, mean ± SE | 1.000 ± 0.065 | 0.900 ± 0.133 | 0.780 ± 0.043 | 0.088* |
| Lipid layer color, NO. (%) | 0.060 | |||
| Colorful | 36(80.00) | 9(64.29) | 42(95.45) | |
| Uncolorful | 9(20.00) | 5(35.71) | 2(4.55) | |
| Lipid layer distribution, NO. (%) | 0.003 | |||
| Even | 25(55.56) | 13(92.86) | 40(90.91) | |
| Uneven | 20(44.44) | 1(7.14) | 4(9.09) | |
| Upper MGL, NO. (%) | < 0.001 | |||
| Normal | 3(6.38) | 4(28.57) | 19(47.50) | |
| Deficiency < 1/3 | 23(48.94) | 6(42.86) | 18(45.00) | |
| 1/3 < Deficiency < 2/3 | 14(29.79) | 3(21.43) | 1(2.50) | |
| Deficiency > 2/3 | 7(14.89) | 1(7.14) | 2(5.00) | |
| Lower MGL, NO. (%) | 0.001 | |||
| Normal | 7(14.89) | 7(50.00) | 30(71.43) | |
| Deficiency < 1/3 | 24(51.06) | 5(35.71) | 8(19.05) | |
| 1/3 < Deficiency < 2/3 | 11(23.40) | 2(14.29) | 1(2.38) | |
| Deficiency > 2/3 | 5(10.64) | 0(0.00) | 3(7.14) |
Abbreviations: SS Sjögren’s Syndrome, NSS Non-Sjögren’s Syndrome dry eye, NC Normal control, n number of eyes, BCVA best-corrected visual acuity, LogMAR logarithmic minimum angle of resolution, IOP intraocular pressure, OSDI ocular surface disease index, CDEQ Chinese dry eye questionnaire, TMH tear meniscus height, OSS Oxford Staining Score, FNIBUT first noninvasive tear break-up time, MNIBUT mean noninvasive tear break-up time, BCH bulbal conjunctival hyperemia, MGL meibomian gland loss
Bold p-value represents < 0.05. * indicates that the data is the result of a Kruskal–Wallis tests
Fig. 2.
Receiver Operating Characteristic (ROC) and area under the curve (AUC) of statistically significant indicators to diagnose SS. OSS, Oxford Staining Score; CDEQ, Chinese Dry Eye Questionnaire; FNIBUT and MNIBUT, first and mean noninvasive tear break-up time
The results of the analysis of the indicators related to corneal optical quality are presented in Table 3. Angle α (p = 0.001), posterior corneal surface astigmatism (p = 0.001), and the types of astigmatism on the anterior (p = 0.0211) surface of the cornea were significantly different between the groups.
Table 3.
Visual performance related indicators of included participants in three groups
| Characteristics | SS(47 eyes) | NSS(14eyes) | NC(44 eyes) | P |
|---|---|---|---|---|
| tHOAs, mean ± SE | 0.240 ± 0.064 | 0.165 ± 0.018 | 0.494 ± 0.064 | 0.807* |
| Coma, mean ± SE | 0.107 ± 0.023 | 0.086 ± 0.015 | 0.260 ± 0.064 | 0.280* |
| Spherical Aberration, mean ± SE | 0.036 ± 0.023 | 0.026 ± 0.010 | −0.018 ± −0.064 | 0.469* |
| Trefoil, mean ± SE | 0.132 ± 0.036 | 0.080 ± 0.129 | 0.263 ± 0.064 | 0.799* |
| Angle α, mean ± SE | 0.365 ± 0.019 | 0.384 ± 0.020 | 0.237 ± 0.064 | 0.001* |
| Angle κ, mean ± SE | 0.361 ± 0.023 | 0.258 ± 0.034 | 0.250 ± 0.064 | 0.820* |
| MTF, mean ± SE | 0.564 ± 0.019 | 0.593 ± 0.344 | 0.544 ± 0.064 | 0.533* |
| Corneal astigmatism | ||||
| Anterior surface of the cornea, mean ± SE | 0.806 ± 0.078 | 1.471 ± 0.268 | 0.857 ± 0.065 | 0.099 |
| Posterior surface of the cornea, mean ± SE | 0.257 ± 0.020 | 0.486 ± 0.051 | 0.330 ± 0.027 | 0.001 |
| Types of astigmatism on the anterior surface of the cornea, NO. (%) | 0.021 | |||
| Regular astigmatism | 32(68.09) | 13(92.86) | 38(86.36) | |
| Irregular astigmatism | 8(17.02) | 1(7.14) | 0(0.00) | |
| Oblique axis astigmatism | 7(14.89) | 0(0.00) | 6(13.64) | |
| Types of astigmatism on the posterior surface of the cornea, NO. (%) | 0.329 | |||
| Regular astigmatism | 45(95.74) | 13(92.86) | 44(100.00) | |
| Irregular astigmatism | 1(2.13) | 1(7.14) | 0(0.00) | |
| Oblique axis astigmatism | 1(2.13) | 0(0.00) | 0(0.00) | |
Abbreviations: SS Sjögren’s Syndrome, NSS Non-Sjögren’s Syndrome dry eye, NC Normal control, n number of eyes, tHOAs total corneal high-order aberrations, MTF modulation transfer function
Bold P-value represents < 0.05. * indicates that the data is the result of a Kruskal–Wallis tests
The P-values in Tables 1, 2 and 3 represented the statistical significance of differences between the three groups (SSDE, NSSDE, and normal controls) for each variable. These values indicated whether there were significant differences in ocular surface manifestations and corneal optical quality indicators among the groups.
Correlation of ocular surface manifestations and visual performance
Correlation analyses were performed to assess the relationship between ocular surface manifestations and corneal optical quality indicators. Significant correlations identified in this step were further explored using multiple linear regression and multinomial logistic regression analyses to quantify the effect size and adjust for potential confounders (Table 4). Results confirmed a statistically robust association between Angle α and dry eye (DE) (Tau-b = 0.324, p < 0.001), while other parameters showed no significant correlations. We used multinomial linear regression analysis to investigate the relationship between DE and Angle α. All ocular surface indicators demonstrating statistically significant differences were incorporated as confounding factors in the regression model construction to control for potential bias.
Table 4.
Correlation analysis between Statistically significant optical quality characteristics and dry eye
| Characteristics | Tau-b | P |
|---|---|---|
| Angle α | 0.324 | < 0.001 |
| PCSA | −0.156 | 0.062 |
| ACSAT | 0.178 | 0.055 |
Abbreviations: DE dry eye, PCSA Posterior corneal surface astigmatism, ACSAT anterior corneal surface astigmatism type, Tau-b Kendall's tau-b rank correlation coefficient
Bold p-value represents < 0.05
The groups of patients were not age-matched, so age and BCVA were treated as confounders in model 1. In Model 2, OSS, FNIBUT, MNIBUT, Schirmer I test, lipid layer distribution, and upper and lower MGL were also considered as confounders. The β-values of these models and their 95% confidence intervals, as well as the p-values, are presented in Table 5. The results showed that DE had a correlation with angle α, and this correlation was more pronounced in patients with angle α ≤ 0.3 (β = 0.062, 95% CI: 0.016,0.108; P = 0.01) than in patients with angle α > 0.3 (β = 0.048, 95% CI: 0.034,0.941; P = 0.035). This linear correlation still remained in Model I, but was no longer statistically significant in Model 2.
Table 5.
Association between Angle α and dry eye in different models
| Presence of dry eye | Events(%) | Crude model | Model I | Model 2 | |||
|---|---|---|---|---|---|---|---|
| β(95%CI) | P | β(95%CI) | P | β(95%CI) | P | ||
| Angle α | |||||||
| ≤ 0.3 | 43.81 | 0.062(0.016,0.108) | 0.010 | 0.089(0.025,0.153) | 0.008 | 0.149(0.025,0.272) | 0.021 |
| > 0.3 | 56.19 | 0.048(0.034,0.941) | 0.035 | 0.068(0.006,0.129) | 0.031 | 0.032(−0.116,0.181) | 0.653 |
Abbreviations: OR odds ratio, CI Confidence interval, OSS Oxford Staining Score, CDEQ Chinese dry eye questionnaire, FNIBUT first non-invasive tear break-up time, MNIBUT mean non-invasive tear break-up time, Upper MGL upper meibomian gland loss, Lower MGL Lower meibomian gland loss. Bold p-value represents < 0.05
Discussion
DE is now a major social and personal economic burden in some developed countries [22]. According to epidemiologic surveys, it is more prevalent in Asia than in Europe and North America [23]. Women are the majority of patients with this disease, and the risk of the disease increases with age [24]. In addition, female patients tend to be diagnosed at a younger age and have more severe symptoms compared to men [25].
As an autoimmune disease that affects multiple organ systems [26], SS is also more predominant in females [27, 28]. Studies have shown that SS causes chronic inflammation of the exocrine glands, which results in tissue destruction and dryness. Consequently, SS patients experience a decrease in aqueous tear production or secretion and are at a greater risk of developing DE.
SSDE can affect the tear film and ocular surface. Previously, researchers have compared dry eye performance between SS patients and healthy individuals. Numerous clinical studies have demonstrated ocular surface changes in SS patients in some classical clinical indicators, including decreased tear production, decreased tear break-up times (TBUT), and hyperosmolarity of tears [29, 30]. A study by J. Shimazaki et al. [31] stated that the lower eyelids of SS patients exhibited more pronounced MGL than other patients. All of these findings are in accordance with our results. Previous studies have shown that an AUC of 0.7–0.8 indicates that the indicator has acceptable discriminatory power [32]. In this study, we have found that Schirmer I test, OSS, CDEQ, FNIBUT and MNIBUT all exhibited robust discriminatory capacity, which were beneficial to distinguish patients with SS.
Destruction of tear film and ocular surface can impair the corneal optical quality, which is the main cause of vision loss in patients with DE. In order to achieve clear vision, it is important to maintain the integrity and stability of the precorneal tear film [33, 34]. In patients with DE, optical aberrations resulting from decreased tear stability and increased tear film breakup may negatively affect the observed image objectively and psychologically. Our study illustrated that, indicators related to optical quality, such as angle α, showed statistical differences in SSDE patients compared to healthy controls. Astigmatism is a common symptom of DE [35]. Furthermore, DE patients have higher tHOA as a consequence of the increased irregularities in their tear film [36]. In the current study, the iTrace is the only ophthalmic instrument capable of measuring both the angles α and κ, enabling the assessment of visual quality in patients with DE disease. Postoperative follow-up results of some ophthalmic surgeries have shown that excessively large angles α can lead to deviation of the visual axis center, reducing the patient's visual quality [37, 38]. Our findings highlight the significant impact of SSDE on corneal optical quality and ocular surface health. The identified correlations between ocular surface parameters and corneal optical quality suggest potential biomarkers for early intervention.This study demonstrates that SSDE significantly impairs corneal optical quality and ocular surface health. Early detection and management of SSDE are crucial to preserving visual quality.
In the present study, some patients completed questionnaires. The ocular surface disease index questionnaire (OSDI) has been employed as a diagnostic and grading tool for DE in numerous studies [39–41]. However, this indicator was not statistically different between the two groups, this may be due to the small number of participants who completed the OSDI survey in this study. The Chinese Dry Eye Questionnaire (CDEQ) is designed for the characteristics of the living and working environments of Chinese, which makes it more suitable for Chinese patients with DE [42]. Combined with our findings, this questionnaire may be more accurate than the OSDI in assessing the ocular surface of Chinese patients.
Goal of this paper is to examine corneal surface symptoms and measurements in a Chinese SSDE group compared to non-SS DE patients and normal controls, and then to hopefully identify specific corneal surface symptoms and indicators that are more helpful in predicting optical quality. The strengths of our study are the inclusion of female patients with SSDE at a certain age and the wide variety of clinical indicators involved. These will greatly benefit patients with SS in terms of protecting their visual quality. This study identified significant correlations between ocular surface manifestations and corneal optical quality in Chinese female patients with SSDE. The findings highlight the importance of early detection and management of dry eye symptoms to preserve visual quality. Future research should explore the underlying mechanisms and potential therapeutic interventions to improve outcomes for patients with SSDE.
In spite of this, there are some limitations to this study. First, some participants provided incomplete or omitted information, which may have affected study results. In addition, the imbalance in the number of participants included in each group may cause some bias in the results. Second, although we have adjusted for the indicators, we have not completely eliminated the effects of unmeasured confounders. Another issue is that the number of cases in the groups of participants we included, as well as their ages, were not matched. Last but not least, this was a cross-sectional study. There is still a need for prospective and experimental studies to determine the causal relationship between visual performance and clinical ocular surface performance in patients with SS.
Conclusion
In conclusion, this study underscores the substantial impact of Sjogren's syndrome dry eye (SSDE) on corneal optical quality and ocular surface health. Our findings highlight the critical importance of early detection and proactive management of SSDE in order to effectively preserve visual quality and mitigate potential long-term complications.
Supplementary Information
Acknowledgements
Support of this work were provided by Guangzhou Science and Technology Project, Natural Science Foundation Project of Guangdong Province, and Guangzhou Health Science and Technology Project.
Authors’ contributions
Author Contributions: JZ and ZL designed the study; QD, MM, LW, YL QH, and SY participated in data collection, analysis and interpretation; JZ, AH, and ZL drafted the manuscript; All authors reviewed and approved the manuscript.
Funding
This study was supported by Guangzhou Science and Technology Project (No. 2024A03J0207), Natural Science Foundation Project of Guangdong Province (No. 2019A1515011094), and Guangzhou Health Science and Technology Project (No. 205010606044).
Data availability
The datasets used and/or analyzed in this study are available upon request from the corresponding author.
Declarations
Ethics approval and consent to participate
This study was performed according to the principles of the Declaration of Helsinki and was approved by the Ethics Committee of the Second Hospital of Guangzhou Medical University. All participants signed a written informed consent before participating in the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
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Jingyu Zhang, Qian Deng and Maierhaba Maitiyaer contributed equally and should be considered as co-first authors.
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ShuiLian Yu, Email: shuilian2008@gmail.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analyzed in this study are available upon request from the corresponding author.